Method and Apparatus for Equalizing Signals

ABSTRACT

pa A system and apparatus are disclosed for a method and apparatus for equalizing signals. An apparatus that incorporates teachings of the present disclosure may include, for example, an equalizer having a channel estimation calculator for calculating a time domain channel estimation from a baseband signal, a Fast Fourier Transform processor for translating the time domain channel estimation to a frequency domain channel estimation, a tap weight calculator for calculating a frequency domain tap weight according to the frequency domain channel estimation, an inverse Fast Fourier Transform processor for translating the frequency domain tap weight calculation to a time domain tap weight calculation, and a filter for equalizing the baseband signal according to the time domain tap weight calculation.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/358,899, filed Nov. 22, 2016, which is a continuation of U.S. patentapplication Ser. No. 14/482,280, filed Sep. 10, 2014 (now U.S. Pat. No.9,553,740, issued on Jan. 24, 2017), which is a continuation of U.S.patent application Ser. No. 13/677,464, filed November 15, 2012 (nowU.S. Pat. No. 8,861,575, issued on October, 14, 2014), which is acontinuation of U.S. patent application Ser. No. 11/225,635, filed Sep.13, 2005 (now U.S. Pat. No. 8,345,733, issued on Jan. 1, 2013). Allsections of the aforementioned application(s) and patent(s) areincorporated herein by reference in their entirety.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to equalizers utilized incommunication systems, and more specifically to a method and apparatusfor equalizing signals.

BACKGROUND

In recent years high speed communications in excess of 10 Mbps hasrapidly spread to cellular systems, broadband systems for residentialenvironments, WiFi hotspots such as coffee shops, and so on. Some ofthese higher communication speeds based on single carriers systems suchas CDMA, GSM, TDMA, WCDMA, etc., require a receiver architecture moreadvanced than a typical Rake receiver in order to function inenvironments with a large delay spread.

One such commonly accepted receiver architecture that is more resilientto multipath distortion is based on a Linear Minimum Mean Squared Error(LMMSE) equalizer. One of the main issues with LMMSE equalization,however, is the need for inverting relatively large matrices, whichmanifests itself in the form of computational and cost overhead.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an equalizer incorporating teachings of thepresent disclosure;

FIG. 2 is a block diagram of communication device incorporatingteachings of the present disclosure;

FIG. 3 depicts a flowchart of a method operating in the communicationdevice incorporating teachings of the present disclosure;

FIGS. 4-6 depict simulations comparing a prior art equalizer with theequalizer of the present disclosure;

FIGS. 7-8 depict tabulations of computational complexity between a priorart equalizer and the equalizer of the present disclosure; and

FIG. 9 is a diagrammatic representation of a machine in the form of acomputer system within which a set of instructions, when executed, maycause the machine to perform any one or more of the methodologiesdiscussed herein.

DETAILED DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an equalizer 100 incorporating teachings ofthe present disclosure. The equalizer 100 comprises a channel estimationcalculator 102, a Fast Fourier Transform (FFT) processor 104, a tapweight calculator 106, an inverse FFT (IFFT) 108, and a filter 110. Theequalizer 100 can be incorporated into a communication device 200 suchas illustrated in FIG. 2 according to the teachings of the presentdisclosure.

The communication device 200 can comprise a transceiver 202, a display204, and audio system 206, and a controller 208. The transceiver 202 canbe used as a wireless or wireline communication element for processingbaseband signals. In wireless applications, the transceiver 202 can beused for down-converting a carrier signal to a baseband signal in adownlink operation, and up-convert a baseband signal to the carriersignal for an uplink operation for communications such as with a remotedevice.

The display 204 can utilize technology such as an LCD (Liquid CrystalDisplay) to convey images to an end user of the communication device200. The audio system 206 utilizes common technology for interceptingand/or conveying audible signals from said users. The controller 208performs signal processing on the baseband signal and manages control ofthe communication device 2000 according to the teachings of the presentdisclosure. The controller 208 can comprise a microprocessor, a digitalsignal processor (DSP), an ASIC (Application Specific IntegratedCircuit), or combinations thereof, with one or more correspondingmemories for storage and data manipulations.

Referring back to FIG. 1, the aforementioned equalizer 100 can beintegrated into the transceiver 202 or the controller 208 of thecommunication device 200 in whole or in part as a hardware and/orsoftware component performing the functions described in steps 304-312of FIG. 3. With this in mind, FIG. 3 depicts a flowchart of a method 300operating in the communication device 200 incorporating teachings of thepresent disclosure. Method 300 begins with step 302 where thetransceiver 202 down-converts an intercepted wireless (or wireline)carrier signal to a baseband signal 101. The baseband signal 101 canrepresent a data signal, video signal, audio signal, or combinationsthereof. In step 304, the channel estimation calculator 102 calculates atime domain channel estimation from the baseband signal 101. In step306, the FFT processor 104 translates the time domain channel estimationto a frequency domain channel estimation. The tap weight calculator 106calculates in step 308 a frequency domain tap weight from the frequencydomain channel estimation.

The IFFT processor 108 in step 310 translates the frequency domain tapweight calculation to a time domain tap weight calculation. In step 312,the filter 110 equalizes the baseband signal 101 according to the timedomain tap weight calculation using a linear filter, thereby producing asignal 105 that restores signal integrity to the baseband signal 101.The functional blocks 102-110 of equalizer 100 as described by theforegoing steps and FIG. 1 can operate as any one of a group ofequalizers comprising a Minimum Mean Squared Error (MMSE) equalizer, aDecision Feedback (DF) equalizer, a Least Mean Square (LMS) equalizer,or a Recursive Least Square (RLS) equalizer. Once any one of theseequalizer embodiments has generated an equalized signal 105, thecontroller 208 can process said signal and thereby convey audio and/orvisual signals in step 314 to a user of the communication device 200from the audio system 206 and/or display 204, respectively.

The aforementioned communication device 200 can be represented by anynumber of embodiments such as, for example, wireless mobile device (likea cellular phone or wireless PDA), a cable transceiver (such as aset-top box), a modem (such as a cable or DSL modem), a Voice over IP(VoIP) handset, and a POTS (Plain Old Telephone) handset, just tomention a few.

The discussions that follow provide a brief mathematical overview of theequalizer 100 as depicted in FIGS. 1-3. The following discussion assumesthe equalizer 100 operating in the communication device 200 processessignals in a WCDMA/HSDPA (Wideband Code Division Multiple Access/HighSpeed Downlink Packet Access) communication system.

Prior art LMMSE equalizers that perform calculations entirely in thetime domain (herein referred to as time-domain LMMSE equalizers) need toinvert a rather large matrix in order to calculate optimum filter taps.The size of the matrix that needs to be inverted is E by E, where E isthe number of taps in the equalizer. The number of taps is a function ofthe delay spread of the channel. In the case of a Pedestrian Benvironment the channel impulse response extends over 16 chips for anHSDPA system, and the optimum number of taps in the equalizer is between24 and 30. Fewer taps leads to degradation in performance and more tapsdoes not provide any extra performance benefit but increasescomputational complexity. In a mobile environment where the channelstate changes rapidly (particularly, for vehicular speeds) the receiverwill be required to calculate optimum equalizer taps very frequentlywhich puts a significant computational overhead on the receiver thusmaking the time-domain LMMSE equalizer an expensive solution toimplement in mobile handsets.

Method 300 proposes a technique for calculating the optimal filterweights without the need for explicit matrix inversion and withoutcompromising the performance in mobile channels. In the time-domainLMMSE equalizer the signal output of the equalizer is given by:

$\begin{matrix}{y_{i} = {{\sum\limits_{j = 0}^{E - 1}{f_{j}r_{i - j}}} = {{\sum\limits_{j = 0}^{E - 1}{\sum\limits_{l = 0}^{L - 1}{f_{j}h_{l}p_{i - j - l}}}} + {\sum\limits_{j = 0}^{E - 1}{f_{j}n_{i - j}}}}}} & (1)\end{matrix}$

According to method 300 this equation can be represented in thefrequency domain, where convolution is replaced by product. Thevariables in the frequency domain are therefore given by:

$\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}\begin{matrix}{Y_{i} = {\sum\limits_{n = 0}^{N - 1}{y_{n}e^{{- j}\; 2\; \pi \frac{ni}{N}}}}} \\{X_{i} = {\sum\limits_{n = 0}^{N - 1}{x_{n}e^{{- j}\; 2\; \pi \frac{ni}{N}}}}}\end{matrix} \\{H_{i} = {\sum\limits_{n = 0}^{N - 1}{h_{n}e^{{- j}\; 2\; \pi \; \frac{ni}{N}}}}}\end{matrix} \\{F_{i} = {\sum\limits_{n = 0}^{N - 1}{f_{n}e^{{- j}\; 2\; \pi \frac{ni}{N}}}}}\end{matrix} \\{B_{i} = {\sum\limits_{n = 0}^{N - 1}{f_{n}e^{{- j}\; 2\; \pi \frac{ni}{N}}}}}\end{matrix} & (2)\end{matrix}$

Thus in the frequency domain equation (1) can be represented as:

(3)

In the frequency domain the mean square error is given by:

$\begin{matrix}\begin{matrix}{\psi_{i} = {\langle{\left( {Y_{i} - {\overset{\sim}{Y}}_{i}} \right)\left( {Y_{i}^{*} - {\overset{\sim}{Y}}_{i}^{*}} \right)}\rangle}} \\{= {{F_{i}{F_{i}^{*}\left( {{H_{i}H_{i}^{*}} + \frac{\sigma^{2}}{p}} \right)}} - {B_{i}F_{i}^{*}H_{i}^{*}} - {B_{i}^{*}F_{i}H_{i}}}}\end{matrix} & (4)\end{matrix}$

It should be noted that averaging is not done over the index i (which isthe frequency bin) but rather averaging is performed from one frame tothe next. Thus the MMSE criteria corresponds to choosing optimal weightsof the equalizer taps, F_(i) such that the MSE (Mean Squared Error) ofeach frequency bin is minimized. However since the MSE of a frequencybin Ψ_(i) depends only on F_(i),the only relevant terms of the MMSEcriteria are:

$\begin{matrix}{\frac{\partial\psi_{i}}{\partial F_{i}} = {{{F_{i}^{*}\left( {{H_{i}H_{i}^{*}} + \frac{\sigma^{2}}{p}} \right)} - {B_{i}^{*}H_{i}}} = 0}} & (5)\end{matrix}$

In order to solve equation (5) properly the equalizer 100 is constrainedto have a finite number of taps in the time domain, i.e., f_(i)=0 fori>E. This constraint can be added to equation (5) in order to provide asolution that is the equivalent of solving the MMSE criteria in the timedomain as outlined in method 300. In order to include the finite spreadof the equalizer in the time domain, equation (4) is modified by thefollowing expression:

$\begin{matrix}{{F_{i} = {{\sum\limits_{l = 0}^{E - 1}{f_{l^{e}}}^{\;^{{- 2}\pi \; j\frac{il}{N}}}} = {{\sum\limits_{l = 0}^{E - 1}{f_{l}\theta_{li}\mspace{20mu} i}} = 0}}},1,\ldots \mspace{14mu},{N - 1}} & (6)\end{matrix}$

In equation (6) θ is a more compact notation for the exponential termsthat are involved with FFT and IFFT operation as discussed in method300. This notation has been chosen to keep the equations more compactand easy to understand. Using this explicit form of F_(i), the meansquare error for each frequency bin is given by:

$\begin{matrix}{\psi_{i} = {{\sum\limits_{k,{l = 0}}^{E - 1}{f_{k}{f_{l}^{*}\left( {\theta_{ki}\theta_{li}^{*}} \right)}\left( {{H_{i}H_{i}^{*}} + \frac{\sigma^{2}}{p}} \right)}} - {\sum\limits_{k = 0}^{E - 1}{f_{k}^{*}\theta_{ki}^{*}B_{i}H_{i}^{*}}} - {\sum\limits_{k = 0}^{E - 1}{f_{k}\theta_{ki}B_{i}^{*}H_{i}}}}} & (7)\end{matrix}$

The MMSE criteria, when applied to each frequency bin leads to thefollowing set of conditions that must be satisfied by the optimumsolution:

$\begin{matrix}{{\sum\limits_{l = 0}^{E - 1}{{f_{l}^{*}\left( {\theta /_{ki}\theta_{li}^{*}} \right)}\left( {{H_{i}H_{i}^{*}} + \frac{\sigma^{2}}{p}} \right)}} = {{\theta /_{ki}B_{i}^{*}}H_{i}}} & (8)\end{matrix}$

This set of conditions can be expressed more compactly by using matrixnotation as:

$\begin{matrix}\begin{matrix}{{f^{H}\Theta^{H}} = {\left. D^{H}\Rightarrow f \right. = {{\overset{\sim}{\Theta}}^{- l}D}}} \\{D_{i} = \frac{B_{i}^{*}H}{\left( {{H_{i}H_{i}^{*}} + \frac{\sigma^{2}}{p}} \right)}}\end{matrix} & (9)\end{matrix}$

Since both H_(i) and B_(i) are scalars this calculation of the MMSEcriteria in the frequency domain does not require any matrix inversionbut rather simple multiplications and division as shown in equation (9).Since the pseudo inverse of Θ^(H) is known a-priori it does not have tobe calculated explicitly at the receiver.

The equalizer 100 described above is different from prior art equalizersoperating entirely in the time domain or frequency domain such as inOrthogonal Frequency Division Modulation (OFDM) systems. According tomethod 300 the taps of equalizer 100 are calculated in the frequencydomain, which when compared to prior art systems avoids conversion of awhole data frame/slot to the frequency domain in a communication systemsuch as WCDMA/HSDPA. Since the equalizer depth is typically much smallerthan slot lengths, the required FFT size with method 300 is smaller.That is, in the case of a WCDMA/HSDPA system each slot consists of 2560samples. If the entire equalization is done in frequency domain (such asin prior art systems) a 2560 point FFT would be required. With method300 only a 64 point FFT would be needed even in the most dispersivechannel such as Pedestrian B.

FIGS. 4-6 depict simulations comparing a prior art equalizer with theequalizer of FIGS. 1-3 according to the teachings of the presentdisclosure. These simulations compare the performance of the time-domainLMMSE equalizer and the equalizer 100 of the present disclosure based onlink level simulations of HSDPA. The transmitted signal for thesesimulations consists of control channels (CPCIH, PCCPCH, SCH, PICH) andan HS-DSCH with 15 code transmission. A total of three communicationdevices 200 (embodied as cellular phones) were code multiplexed at agiven time with each communication device 200 having 5 codes. The datafor an additional two communication devices 200 occupying the remainderof the 10 codes channels, acted as the OCNS along with 4 HS-SCCH whichwere modeled explicitly. The HS-DSCH (all 15 codes) is allocated 80% ofthe power (˜−1 dB E_(c)/I_(or)), and the remainder 20% of the power isshared by the control channels and the HS-SCCH channels.

The composite signal at the transmitter is converted into the analogdomain signal by using a transmission filter and 4× up-sampling. Thetransmission filter used is the same as specified by the 3GPPspecifications. The up-sampled signal is taken through a multipathfading channel. Other cell interference I_(oc), modeled as AWGN wasadded to the signal at the receiver. The variance of I_(oc), wasadjusted to model a desired geometry (I_(or)/I_(oc)).

The received signal is down-sampled by 4× using a match filter and thenis used according to a realistic channel/noise variance estimation andtracking algorithms. Channel estimates are used rather than the perfectknowledge to accurately model the performance of an HSDPA receiver inthe real world. The channel and noise variance estimates are used tocalculate the optimum equalizer taps and to generate the LLR (log-likelihood-ratio) of the soft bits. The LLRs are then used by the TurboDecoder and H- ARQ stage to estimate the information bits.

Finally for each transport block the received information bits arecompared against the transmitted information bits to determine if aH-ARQ retransmission is needed. If there are no errors then thetransport block is marked as received without error. However, if errorare found and the maximum number of H-ARQ transmissions is not reached,then the block is retransmitted with a 12 millisecond delay anddifferent redundancy version, if the maximum number of H-ARQ is notreached. On the other hand, if the block is received with errors and themaximum number of H-ARQ transmission is reached then the transport ismarked as received with error. The maximum number of allowed H-ARQtransmission is set at 4 for the purposes of these simulations.

The average throughput for a given I_(or)/I_(oc) and E_(c)/I_(or)combination is a function of the transport block size, average number ofretransmission required and the BLER (block error rate) at the end ofthe maximum number of H-ARQ transmission given by:

$\begin{matrix}{T = {\frac{M}{{2e} - 3}\frac{\left( {1 - {B\; L\; E\; R}} \right)}{NTRANS}}} & (10)\end{matrix}$

where T is the transport block size, BLER is the block error rate after4 H-ARQ transmissions and NTRANS is the average number of H-ARQ requiredper transport block.

FIG. 4 depicts the throughput of a 5 code communication device 200operating in QPSK Mode with a transport block size of 2362 bits. FIG. 5depicts the throughput of a 5 code communication device 200 operating ina 16 QAM Mode with a transport size of 4420 bits. FIG. 6 depicts the BER(Bit Error Rate) comparison between a time-domain LMMSE equalizer andequalizer 100 as described in FIGS. 1-3 in Ped A and Ped B environments.Since a time-domain LMMSE equalizer and equalizer 100 are based on thesame mathematical principle but expressed in different domains, there isno expected performance difference between them. This is supported bysimulation results under various conditions and channel models. In spiteof similar performance the two approaches differ vastly in theircomputational complexity.

FIG. 7 depicts a table of the number of multiplications, additions anddivisions required by each of the time-domain LMMSE equalizer andequalizer 100 as a function of L, E, and N which are the memory of thechannel, number of taps in the equalizer, and FFT size. FIG. 8 depicts atable of the number of operations required by the time-domain LMMSEequalizer and equalizer 100 for calculating the optimum tap weights forthe Ped A and Ped B environment.

From FIGS. 7-8 it is apparent that equalizer 100 is significantly lesscomplex than the prior art time-domain LMMSE equalizer with comparableperformance. The foregoing simulation results show virtually nodifference between the performance of the time-domain LMMSE equalizerand equalizer 100. Depending on the environment, the equalizer 100 isable to achieve the same level of performance with ( 1/25)^(th) thecomplexity that of the prior art time-domain LMMSE equalizer.

FIG. 9 is a diagrammatic representation of a machine in the form of acomputer system 900 within which a set of instructions, when executed,may cause the machine to perform any one or more of the methodologiesdiscussed above. In some embodiments, the machine operates as astandalone device. In some embodiments, the machine may be connected(e.g., using a network) to other machines. In a networked deployment,the machine may operate in the capacity of a server or a client usermachine in server-client user network environment, or as a peer machinein a peer-to-peer (or distributed) network environment. The machine maycomprise a server computer, a client user computer, a personal computer(PC), a tablet PC, a laptop computer, a desktop computer, a controlsystem, a network router, switch or bridge, or any machine capable ofexecuting a set of instructions (sequential or otherwise) that specifyactions to be taken by that machine. It will be understood that a deviceof the present disclosure includes broadly any electronic device thatprovides voice, video or data communication. Further, while a singlemachine is illustrated, the term “machine” shall also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions to perform any one or more ofthe methodologies discussed herein.

The computer system 900 may include a processor 902 (e.g., a centralprocessing unit (CPU), a graphics processing unit (GPU, or both), a mainmemory 904 and a static memory 906, which communicate with each othervia a bus 908. The computer system 900 may further include a videodisplay unit 910 (e.g., a liquid crystal display (LCD), a flat panel, asolid state display, or a cathode ray tube (CRT)). The computer system900 may include an input device 912 (e.g., a keyboard), a cursor controldevice 914 (e.g., a mouse), a disk drive unit 916, a signal generationdevice 918 (e.g., a speaker or remote control) and a network interfacedevice 920.

The disk drive unit 916 may include a machine-readable medium 922 onwhich is stored one or more sets of instructions (e.g., software 924)embodying any one or more of the methodologies or functions describedherein, including those methods illustrated in herein above. Theinstructions 924 may also reside, completely or at least partially,within the main memory 904, the static memory 906, and/or within theprocessor 902 during execution thereof by the computer system 900. Themain memory 904 and the processor 902 also may constitutemachine-readable media. Dedicated hardware implementations including,but not limited to, application specific integrated circuits,programmable logic arrays and other hardware devices can likewise beconstructed to implement the methods described herein. Applications thatmay include the apparatus and systems of various embodiments broadlyinclude a variety of electronic and computer systems. Some embodimentsimplement functions in two or more specific interconnected hardwaremodules or devices with related control and data signals communicatedbetween and through the modules, or as portions of anapplication-specific integrated circuit. Thus, the example system isapplicable to software, firmware, and hardware implementations.

In accordance with various embodiments of the present disclosure, themethods described herein are intended for operation as software programsrunning on a computer processor. Furthermore, software implementationscan include, but not limited to, distributed processing orcomponent/object distributed processing, parallel processing, or virtualmachine processing can also be constructed to implement the methodsdescribed herein.

The present disclosure contemplates a machine readable medium containinginstructions 924, or that which receives and executes instructions 924from a propagated signal so that a device connected to a networkenvironment 926 can send or receive voice, video or data, and tocommunicate over the network 926 using the instructions 924. Theinstructions 924 may further be transmitted or received over a network926 via the network interface device 920.

While the machine-readable medium 922 is shown in an example embodimentto be a single medium, the term “machine-readable medium” should betaken to include a single medium or multiple media (e.g., a centralizedor distributed database, and/or associated caches and servers) thatstore the one or more sets of instructions. The term “machine-readablemedium” shall also be taken to include any medium that is capable ofstoring, encoding or carrying a set of instructions for execution by themachine and that cause the machine to perform any one or more of themethodologies of the present disclosure.

The term “machine-readable medium” shall accordingly be taken toinclude, but not be limited to: solid-state memories such as a memorycard or other package that houses one or more read-only (non-volatile)memories, random access memories, or other re-writable (volatile)memories; magneto-optical or optical medium such as a disk or tape; andcarrier wave signals such as a signal embodying computer instructions ina transmission medium; and/or a digital file attachment to e-mail orother self-contained information archive or set of archives isconsidered a distribution medium equivalent to a tangible storagemedium. Accordingly, the disclosure is considered to include any one ormore of a machine-readable medium or a distribution medium, as listedherein and including art-recognized equivalents and successor media, inwhich the software implementations herein are stored.

Although the present specification describes components and functionsimplemented in the embodiments with reference to particular standardsand protocols, the disclosure is not limited to such standards andprotocols. Each of the standards for Internet and other packet switchednetwork transmission (e.g., TCP/IP, UDP/IP, HTML, HTTP) representexamples of the state of the art. Such standards are periodicallysuperseded by faster or more efficient equivalents having essentiallythe same functions. Accordingly, replacement standards and protocolshaving the same functions are considered equivalents.

The illustrations of embodiments described herein are intended toprovide a general understanding of the structure of various embodiments,and they are not intended to serve as a complete description of all theelements and features of apparatus and systems that might make use ofthe structures described herein. Many other embodiments will be apparentto those of skill in the art upon reviewing the above description. Otherembodiments may be utilized and derived therefrom, such that structuraland logical substitutions and changes may be made without departing fromthe scope of this disclosure. Figures are also merely representationaland may not be drawn to scale. Certain proportions thereof may beexaggerated, while others may be minimized. Accordingly, thespecification and drawings are to be regarded in an illustrative ratherthan a restrictive sense.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the above description.

The Abstract of the Disclosure is provided to comply with 37 C.F.R. §1.72(b), requiring an abstract that will allow the reader to quicklyascertain the nature of the technical disclosure. It is submitted withthe understanding that it will not be used to interpret or limit thescope or meaning of the claims. In addition, in the foregoing DetailedDescription, it can be seen that various features are grouped togetherin a single embodiment for the purpose of streamlining the disclosure.This method of disclosure is not to be interpreted as reflecting anintention that the claimed embodiments require more features than areexpressly recited in each claim. Rather, as the following claimsreflect, inventive subject matter lies in less than all features of asingle disclosed embodiment. Thus the following claims are herebyincorporated into the Detailed Description, with each claim standing onits own as a separately claimed subject matter.

What is claimed is:
 1. A device, comprising: a processing systemincluding a processor; and a memory that stores executable instructionsthat, when executed by the processing system, facilitate performance ofoperations, the operations comprising: translating a frequency domaintap weight calculation associated with a signal to a time domain tapweight calculation; and equalizing the signal according to the timedomain tap weight calculation to provide an equalized signal, whereinthe equalizing is performed using an equalization techniquecorresponding to one of a Minimum Mean Squared Error (MMSE) equalizer, aDecision Feedback (DF) equalizer, a Least Mean Square (LMS) equalizer,or a Recursive Least Square (RLS) equalizer.
 2. The device of claim 1,wherein the operations further comprise performing the frequency domaintap weight calculation according to a frequency domain channelestimation.
 3. The device of claim 2, wherein the frequency domainchannel estimation is produced by translating a time domain channelestimation.
 4. The device of claim 3, wherein the time domain channelestimation is calculated from the signal.
 5. The device of claim 3,wherein the operations further comprise: applying a Fast FourierTransform to the time domain channel estimation to produce the frequencydomain channel estimation; and applying an inverse Fast FourierTransform to the frequency domain tap weight calculation to produce thetime domain tap weight calculation.
 6. The device of claim 1, whereinthe signal is a baseband signal, and wherein the equalizing restoressignal integrity to the baseband signal.
 7. The device of claim 1,wherein the device is a mobile device, wherein the mobile devicecomprises a cellular phone, and wherein the cellular phone comprises anantenna coupled to a transceiver.
 8. The device of claim 7, wherein thetransceiver operates in a wideband code division multiple access highspeed downlink packet access communication system.
 9. The device ofclaim 1, wherein a selected equalization technique corresponds to theLeast Mean Square (LMS) equalizer.
 10. The device of claim 1, whereinthe signal is a baseband signal, and wherein the operations furthercomprise converting a carrier signal to the baseband signal.
 11. Anon-transitory machine-readable storage medium comprising executableinstructions that, when executed by a processing system including aprocessor, facilitate performance of operations, the operationscomprising: translating a time domain channel estimation associated witha signal to produce a frequency domain channel estimation; andequalizing the signal according to a time domain tap weight calculationto provide an equalized signal, wherein the time domain tap weightcalculation is based upon a translation of a frequency domain tap weightcalculation, wherein the frequency domain tap weight calculation isaccording to the frequency domain channel estimation, and wherein theequalizing is performed using an equalization technique corresponding toone of a Minimum Mean Squared Error (MMSE) equalizer, a DecisionFeedback (DF) equalizer, a Least Mean Square (LMS) equalizer, or aRecursive Least Square (RLS) equalizer.
 12. The non-transitorymachine-readable storage medium of claim 11, wherein the operationsfurther comprise: applying a Fast Fourier Transform to the time domainchannel estimation to produce the frequency domain channel estimation.13. The non-transitory machine-readable storage medium of claim 11,wherein the signal is a baseband signal, and wherein the equalizingrestores signal integrity to the baseband signal.
 14. The non-transitorymachine-readable storage medium of claim 11, wherein the equalizingcomprises averaging, and wherein the averaging is performed from oneframe to a next frame.
 15. The non-transitory machine-readable storagemedium of claim 11, wherein the signal is a baseband signal, and whereinthe operations further comprise converting a carrier signal to thebaseband signal.
 16. The non-transitory machine-readable storage mediumof claim 15, wherein the carrier signal comprises one of a data signal,a video signal, an audio signal, or a combined video and audio signal.17. The non-transitory machine-readable storage medium of claim 11,wherein the processing system is in a mobile device, wherein the mobiledevice comprises a cellular phone, wherein the cellular phone comprisesa transceiver, and wherein the transceiver operates in a wideband codedivision multiple access high speed downlink packet access communicationsystem.
 18. A method, comprising: translating, by a processing systemincluding a processor, a time domain channel estimation to produce afrequency domain channel estimation, wherein the time domain channelestimation is calculated from a signal; translating, by the processingsystem, a frequency domain tap weight calculation to a time domain tapweight calculation, wherein the frequency domain tap weight calculationis according to the frequency domain channel estimation; and equalizing,by the processing system, the signal according to the time domain tapweight calculation to provide an equalized signal, wherein theequalizing is performed using an equalization technique corresponding toone of a Minimum Mean Squared Error (MMSE) equalizer, a DecisionFeedback (DF) equalizer, a Least Mean Square (LMS) equalizer, or aRecursive Least Square (RLS) equalizer.
 19. The method of claim 18,wherein the signal is a baseband signal, and wherein the equalizingrestores signal integrity to the baseband signal.
 20. The method ofclaim 18, wherein the signal is a baseband signal, wherein the methodfurther comprises converting, by the processing system, a carrier signalto the baseband signal, and wherein the carrier signal comprises one ofa data signal, a video signal, an audio signal, or a combined video andaudio signal.